code: def create(
      dataset,
      target,
      features=None,
      l2_penalty=1e-2,
      l1_penalty=0.0,
      solver="auto",
      feature_rescaling=True,
      convergence_threshold=_DEFAULT_SOLVER_OPTIONS[
          "convergence_threshold"
      ],
      step_size=_DEFAULT_SOLVER_OPTIONS["step_size"],
      lbfgs_memory_level=_DEFAULT_SOLVER_OPTIONS["lbfgs_memory_level"],
      max_iterations=_DEFAULT_SOLVER_OPTIONS["max_iterations"],
      validation_set="auto",
      verbose=True,
  ):
      """
      Create a :class:`~turicreate.linear_regression.LinearRegression` to
      predict a scalar target variable as a linear function of one or more
      features. In addition to standard numeric and categorical types, features
      can also be extracted automatically from list- or dictionary-type SFrame
      columns.
  
      The linear regression module can be used for ridge regression, Lasso, and
      elastic net regression (see References for more detail on these methods). By
      default, this model has an l2 regularization weight of 0.01.
  
      Parameters
      ----------
      dataset : SFrame
          The dataset to use for training the model.
  
      target : string
          Name of the column containing the target variable.
  
      features : list[string], optional
          Names of the columns containing features. 'None' (the default) indicates
          that all columns except the target variable should be used as features.
  
          The features are columns in the input SFrame that can be of the
          following types:
  
          - *Numeric*: values of numeric type integer or float.
  
          - *Categorical*: values of type string.
  
          - *Array*: list of numeric (integer or float) values. Each list element
            is treated as a separate feature in the model.
  
          - *Dictionary*: key-value pairs with numeric (integer or float) values
            Each key of a dictionary is treated as a separate feature and the
            value in the dictionary corresponds to the value of the feature.
            Dictionaries are ideal for representing sparse data.
  
          Columns of type *list* are not supported. Convert such feature
          columns to type array if all entries in the list are of numeric
          types. If the lists contain data of mixed types, separate
          them out into different columns.
  
      l2_penalty : float, optional
          Weight on the l2-regularizer of the model. The larger this weight, the
          more the model coefficients shrink toward 0. This introduces bias into
          the model but decreases variance, potentially leading to better
          predictions. The default value is 0.01; setting this parameter to 0
          corresponds to unregularized linear regression. See the ridge
          regression reference for more detail.
  
      l1_penalty : float, optional
          Weight on l1 regularization of the model. Like the l2 penalty, the
          higher the l1 penalty, the more the estimated coefficients shrink toward
          0. The l1 penalty, however, completely zeros out sufficiently small
          coefficients, automatically indicating features that are not useful for
          the model. The default weight of 0 prevents any features from being
          discarded. See the LASSO regression reference for more detail.
  
      solver : string, optional
          Solver to use for training the model. See the references for more detail
          on each solver.
  
          - *auto (default)*: automatically chooses the best solver for the data
            and model parameters.
          - *newton*: Newton-Raphson
          - *lbfgs*: limited memory BFGS
          - *fista*: accelerated gradient descent
  
          The model is trained using a carefully engineered collection of methods
          that are automatically picked based on the input data. The ``newton``
          method  works best for datasets with plenty of examples and few features
          (long datasets). Limited memory BFGS (``lbfgs``) is a robust solver for
          wide datasets (i.e datasets with many coefficients).  ``fista`` is the
          default solver for l1-regularized linear regression.  The solvers are
          all automatically tuned and the default options should function well.
          See the solver options guide for setting additional parameters for each
          of the solvers.
  
          See the user guide for additional details on how the solver is chosen.
  
      feature_rescaling : boolean, optional
          Feature rescaling is an important pre-processing step that ensures that
          all features are on the same scale. An l2-norm rescaling is performed
          to make sure that all features are of the same norm. Categorical
          features are also rescaled by rescaling the dummy variables that are
          used to represent them. The coefficients are returned in original scale
          of the problem. This process is particularly useful when features
          vary widely in their ranges.
  
      validation_set : SFrame, optional
  
          A dataset for monitoring the model's generalization performance.
          For each row of the progress table, the chosen metrics are computed
          for both the provided training dataset and the validation_set. The
          format of this SFrame must be the same as the training set.
          By default this argument is set to 'auto' and a validation set is
          automatically sampled and used for progress printing. If
          validation_set is set to None, then no additional metrics
          are computed. The default value is 'auto'.
  
      convergence_threshold : float, optional
  
        Convergence is tested using variation in the training objective. The
        variation in the training objective is calculated using the difference
        between the objective values between two steps. Consider reducing this
        below the default value (0.01) for a more accurately trained model.
        Beware of overfitting (i.e a model that works well only on the training
        data) if this parameter is set to a very low value.
  
      lbfgs_memory_level : int, optional
  
        The L-BFGS algorithm keeps track of gradient information from the
        previous ``lbfgs_memory_level`` iterations. The storage requirement for
        each of these gradients is the ``num_coefficients`` in the problem.
        Increasing the ``lbfgs_memory_level`` can help improve the quality of
        the model trained. Setting this to more than ``max_iterations`` has the
        same effect as setting it to ``max_iterations``.
  
      max_iterations : int, optional
  
        The maximum number of allowed passes through the data. More passes over
        the data can result in a more accurately trained model. Consider
        increasing this (the default value is 10) if the training accuracy is
        low and the *Grad-Norm* in the display is large.
  
      step_size : float, optional (fista only)
  
        The starting step size to use for the ``fista`` and ``gd`` solvers. The
        default is set to 1.0, this is an aggressive setting. If the first
        iteration takes a considerable amount of time, reducing this parameter
        may speed up model training.
  
      verbose : bool, optional
          If True, print progress updates.
  
      Returns
      -------
      out : LinearRegression
          A trained model of type
          :class:`~turicreate.linear_regression.LinearRegression`.
  
      See Also
      --------
      LinearRegression, turicreate.boosted_trees_regression.BoostedTreesRegression, turicreate.regression.create
  
      Notes
      -----
      - Categorical variables are encoded by creating dummy variables. For a
        variable with :math:`K` categories, the encoding creates :math:`K-1` dummy
        variables, while the first category encountered in the data is used as the
        baseline.
  
      - For prediction and evaluation of linear regression models with sparse
        dictionary inputs, new keys/columns that were not seen during training
        are silently ignored.
  
      - Any 'None' values in the data will result in an error being thrown.
  
      - A constant term is automatically added for the model intercept. This term
        is not regularized.
  
      - Standard errors on coefficients are only available when `solver=newton`
        or when the default `auto` solver option chooses the newton method and if
        the number of examples in the training data is more than the number of
        coefficients. If standard errors cannot be estimated, a column of `None`
        values are returned.
  
  
      References
      ----------
      - Hoerl, A.E. and Kennard, R.W. (1970) `Ridge regression: Biased Estimation
        for Nonorthogonal Problems
        <http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634>`_.
        Technometrics 12(1) pp.55-67
  
      - Tibshirani, R. (1996) `Regression Shrinkage and Selection via the Lasso <h
        ttp://www.jstor.org/discover/10.2307/2346178?uid=3739256&uid=2&uid=4&sid=2
        1104169934983>`_. Journal of the Royal Statistical Society. Series B
        (Methodological) 58(1) pp.267-288.
  
      - Zhu, C., et al. (1997) `Algorithm 778: L-BFGS-B: Fortran subroutines for
        large-scale bound-constrained optimization
        <https://dl.acm.org/citation.cfm?id=279236>`_. ACM Transactions on
        Mathematical Software 23(4) pp.550-560.
  
      - Barzilai, J. and Borwein, J. `Two-Point Step Size Gradient Methods
        <http://imajna.oxfordjournals.org/content/8/1/141.short>`_. IMA Journal of
        Numerical Analysis 8(1) pp.141-148.
  
      - Beck, A. and Teboulle, M. (2009) `A Fast Iterative Shrinkage-Thresholding
        Algorithm for Linear Inverse Problems
        <http://epubs.siam.org/doi/abs/10.1137/080716542>`_. SIAM Journal on
        Imaging Sciences 2(1) pp.183-202.
  
      - Zhang, T. (2004) `Solving large scale linear prediction problems using
        stochastic gradient descent algorithms
        <https://dl.acm.org/citation.cfm?id=1015332>`_. ICML '04: Proceedings of
        the twenty-first international conference on Machine learning p.116.
  
  
      Examples
      --------
  
      Given an :class:`~turicreate.SFrame` ``sf`` with a list of columns
      [``feature_1`` ... ``feature_K``] denoting features and a target column
      ``target``, we can create a
      :class:`~turicreate.linear_regression.LinearRegression` as follows:
  
      >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
      >>> model = turicreate.linear_regression.create(data, target='price',
      ...                                  features=['bath', 'bedroom', 'size'])
  
  
      For ridge regression, we can set the ``l2_penalty`` parameter higher (the
      default is 0.01). For Lasso regression, we set the l1_penalty higher, and
      for elastic net, we set both to be higher.
  
      .. sourcecode:: python
  
        # Ridge regression
        >>> model_ridge = turicreate.linear_regression.create(data, 'price', l2_penalty=0.1)
  
        # Lasso
        >>> model_lasso = turicreate.linear_regression.create(data, 'price', l2_penalty=0.,
                                                                     l1_penalty=1.0)
  
        # Elastic net regression
        >>> model_enet  = turicreate.linear_regression.create(data, 'price', l2_penalty=0.5,
                                                                   l1_penalty=0.5)
  
      """
  
      # Regression model names.
      model_name = "regression_linear_regression"
      solver = solver.lower()
  
      model = _sl.create(
          dataset,
          target,
          model_name,
          features=features,
          validation_set=validation_set,
          solver=solver,
          verbose=verbose,
          l2_penalty=l2_penalty,
          l1_penalty=l1_penalty,
          feature_rescaling=feature_rescaling,
          convergence_threshold=convergence_threshold,
          step_size=step_size,
          lbfgs_memory_level=lbfgs_memory_level,
          max_iterations=max_iterations,
      )
  
      return LinearRegression(model.__proxy__)
  
file_path: turicreate/src/python/turicreate/toolkits/regression/linear_regression.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate.toolkits._supervised_learning
  from turicreate.toolkits._supervised_learning import SupervisedLearningModel
  from turicreate.toolkits._internal_utils import _toolkit_repr_print, _toolkit_get_topk_bottomk, _summarize_coefficients, _raise_error_evaluation_metric_is_valid
label_desc: Use image node_id 1 for calling a global function with example usage: create(dataset, target, features, l2_penalty, l1_penalty, solver, feature_rescaling, convergence_threshold, step_size, lbfgs_memory_level, max_iterations, validation_set, verbose) and returns: LinearRegression